{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2 as cv\n",
    "import matplotlib.pyplot as plt\n",
    "from skimage.feature import hog\n",
    "from skimage import exposure\n",
    "import os\n",
    "from sklearn import svm\n",
    "import numpy as np\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, average_precision_score, f1_score, recall_score, precision_score\n",
    "\n",
    "%config Completer.use_jedi = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ls(path):\n",
    "    files =  os.listdir(path)\n",
    "    return [os.path.join(path,x) for x in files]\n",
    "\n",
    "positive_files = ls(\"./dataset/positive/\")\n",
    "negative_files = ls(\"./dataset/negative/\")\n",
    "\n",
    "split_pos = int(0.7*len(positive_files))\n",
    "split_neg = int(0.7*len(negative_files))\n",
    "\n",
    "train_positive, test_positive =  positive_files[:split_pos],positive_files[split_pos:]\n",
    "train_negative, test_negative =  negative_files[:split_neg],negative_files[split_neg:]\n",
    "\n",
    "train_positive = [cv.imread(img)[:,:,::-1] for img in train_positive]\n",
    "train_negative = [cv.imread(img)[:,:,::-1] for img in train_negative]\n",
    "\n",
    "test_positive = [cv.imread(img)[:,:,::-1] for img in test_positive]\n",
    "test_negative = [cv.imread(img)[:,:,::-1] for img in test_negative]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n"
     ]
    }
   ],
   "source": [
    "def feature_extraction(img, ppc = 8, cpb = 2):\n",
    "    return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
    "\n",
    "# Training Samples\n",
    "train_positive_hog = [feature_extraction(img) for img in train_positive]\n",
    "train_negative_hog = [feature_extraction(img) for img in train_negative]\n",
    "train_positive_hog = np.array(train_positive_hog)\n",
    "train_negative_hog = np.array(train_negative_hog)\n",
    "\n",
    "# Testing Samples\n",
    "test_positive_hog = [feature_extraction(img) for img in test_positive]\n",
    "test_negative_hog = [feature_extraction(img) for img in test_negative]\n",
    "test_positive_hog = np.array(test_positive_hog)\n",
    "test_negative_hog = np.array(test_negative_hog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine positive and negative samples\n",
    "X = np.concatenate((train_positive_hog,train_negative_hog))\n",
    "X_test = np.concatenate((test_positive_hog,test_negative_hog))\n",
    "\n",
    "# Create labels\n",
    "Y = np.zeros(len(X))\n",
    "Y[:len(train_positive_hog)] = 0\n",
    "Y[len(train_positive_hog):] = 1\n",
    "\n",
    "\n",
    "Y_test = np.zeros(len(X_test))\n",
    "Y_test[:len(test_positive_hog)] = 0\n",
    "Y_test[len(test_positive_hog):] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=0.6, max_iter=10000)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc = svm.LinearSVC(C=0.6,max_iter=10000)\n",
    "svc.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AP: 0.9256036283199668\n",
      "Accuracy: 0.9321774604793472\n",
      "Recall: 0.9530744336569579\n",
      "Precision: 0.9401436552274541\n",
      "F1: 0.9465648854961832\n"
     ]
    }
   ],
   "source": [
    "Y_pred = svc.predict(X_test)\n",
    "print(\"AP:\", average_precision_score(Y_test,Y_pred))\n",
    "print(\"Accuracy:\", accuracy_score(Y_test,Y_pred))\n",
    "print(\"Recall:\", recall_score(Y_test,Y_pred))\n",
    "print(\"Precision:\", precision_score(Y_test,Y_pred))\n",
    "print(\"F1:\", f1_score(Y_test,Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = cv.imread(\"./Images/FudanPed00036.png\")[:,:,::-1]\n",
    "scale = 0.5\n",
    "img = cv.resize(img, (int(scale*img.shape[1]),int(scale*img.shape[0]))) \n",
    "plt.imshow(img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n",
      "C:\\Users\\Cybaster\\AppData\\Local\\Temp/ipykernel_17896/885632129.py:2: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
      "  return hog(img, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(cpb, cpb), visualize=False, multichannel=True)\n"
     ]
    }
   ],
   "source": [
    "height, width = 128, 64\n",
    "def detect(img, svc, step = 10, height = 128, width = 64):\n",
    "    \n",
    "    # Iterate over image and apply classifier\n",
    "    result = np.zeros(img.shape[:2])-1\n",
    "    for i in range(0,img.shape[0]-height, step):\n",
    "        for j in range(0,img.shape[1]-width, step):\n",
    "            result[i,j] = svc.predict(feature_extraction(img[i:i+height,j:j+width]).reshape(1, -1))[0]\n",
    "    \n",
    "    y,x = np.nonzero(result==0)\n",
    "    bounding_boxes = np.stack([x,y,x+width,y+height],1)\n",
    "    return result, bounding_boxes\n",
    "            \n",
    "result, bounding_boxes = detect(img, svc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1f2511901c0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = img.copy()\n",
    "for i in range(len(bounding_boxes)):\n",
    "    res = cv.rectangle(res.astype(np.float32),(bounding_boxes[i,0],bounding_boxes[i,1]),(bounding_boxes[i,2],bounding_boxes[i,3]),(0,255,0),3).astype(np.uint8)\n",
    "plt.imshow(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def non_max_suppression_fast(boxes, overlapThresh):\n",
    "    if len(boxes) == 0:\n",
    "        return []\n",
    "    if boxes.dtype.kind == \"i\":\n",
    "        boxes = boxes.astype(\"float\")\n",
    "\n",
    "    pick = []\n",
    "    x1 = boxes[:,0]\n",
    "    y1 = boxes[:,1]\n",
    "    x2 = boxes[:,2]\n",
    "    y2 = boxes[:,3]\n",
    "    area = (x2 - x1 + 1) * (y2 - y1 + 1)\n",
    "    idxs = np.argsort(y2)\n",
    "    \n",
    "    while len(idxs) > 0:\n",
    "        last = len(idxs) - 1\n",
    "        i = idxs[last]\n",
    "        pick.append(i)\n",
    "        \n",
    "        xx1 = np.maximum(x1[i], x1[idxs[:last]])\n",
    "        yy1 = np.maximum(y1[i], y1[idxs[:last]])\n",
    "        xx2 = np.minimum(x2[i], x2[idxs[:last]])\n",
    "        yy2 = np.minimum(y2[i], y2[idxs[:last]])\n",
    "\n",
    "        w = np.maximum(0, xx2 - xx1 + 1)\n",
    "        h = np.maximum(0, yy2 - yy1 + 1)\n",
    "        overlap = (w * h) / area[idxs[:last]]\n",
    "        idxs = np.delete(idxs, np.concatenate(([last],\n",
    "            np.where(overlap > overlapThresh)[0])))\n",
    "    return boxes[pick].astype(\"int\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "bounding_boxes = non_max_suppression_fast(bounding_boxes, 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1f2511ff4f0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = img.copy()\n",
    "for i in range(len(bounding_boxes)):\n",
    "    res = cv.rectangle(res.astype(np.float32),(bounding_boxes[i,0],bounding_boxes[i,1]),(bounding_boxes[i,2],bounding_boxes[i,3]),(0,255,0),3).astype(np.uint8)\n",
    "plt.imshow(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
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